Dynamic resource allocation for computational simulation

ABSTRACT

Systems and methods for automated resource allocation during a computational simulation are described herein. An example method includes analyzing a set of simulation inputs to determine a first set of computing resources for performing a simulation, and starting the simulation with the first set of computing resources. The method also includes dynamically analyzing at least one attribute of the simulation to determine a second set of computing resources for performing the simulation, and performing the simulation with the second set of computing resources. The second set of computing resources is different than the first set of computing resources.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. patent application Ser. No.17/030,991, filed on Sep. 24, 2020, and titled “DYNAMIC RESOURCEALLOCATION FOR COMPUTATIONAL SIMULATION,” the disclosure of which isexpressly incorporated herein by reference in its entirety.

BACKGROUND

Computer-aided engineering (CAE) is the practice of simulatingrepresentations of physical objects using computational methodsincluding, but not limited to, finite element method (FEM) and finitedifference method (FDM). To perform simulations using FEM and/or FDM,the domain must be discretized into a finite number of elements called amesh. FEM and FDM are techniques for converting differential equations(e.g., partial differential equations (PDEs)) into a system of equationsthat can be solved numerically.

SUMMARY

An example computer-implemented method for automated resource allocationduring a computational simulation is described herein. The methodincludes analyzing a set of simulation inputs to determine a first setof computing resources for performing a simulation, and starting thesimulation with the first set of computing resources. The method alsoincludes dynamically analyzing at least one attribute of the simulationto determine a second set of computing resources for performing thesimulation, and performing the simulation with the second set ofcomputing resources. The second set of computing resources is differentthan the first set of computing resources.

Additionally, in some implementations, the step of dynamically analyzingthe at least one attribute of the simulation further determines that thesimulation requires more computing resources than included in the firstset of computing resources.

Alternatively or additionally, the set of simulation inputs includes atleast one of a geometry representation, a material property, a boundarycondition, a loading condition, a mesh parameter, a solver option, asimulation output request, or a time parameter.

Alternatively or additionally, the at least one attribute of thesimulation is a simulation requirement, a simulation performancecharacteristic, or a compute capacity indicator. The compute capacityindicator includes at least one of a usage level of computing capacity,a memory bandwidth, a network bandwidth, or a network latency.

Optionally, in some implementations, respective simulation inputs foreach of a plurality of simulations are analyzed.

In some implementations, the step of performing the simulation with thesecond set of computing resources includes automatically restarting thesimulation with the second set of computing resources. Alternatively,the step of performing the simulation with the second set of computingresources includes automatically continuing the simulation with thesecond set of computing resources.

Alternatively or additionally, in some implementations, the methodoptionally includes adaptively refining a mesh during the simulation.The adaptive refinement of the mesh includes changing a mesh densityand/or an order of mesh elements.

Alternatively or additionally, in some implementations, the set ofsimulation inputs is analyzed to determine the first set of computingresources for performing the simulation while achieving a target valuefor a simulation metric. Alternatively or additionally, in someimplementations, the at least one attribute of the simulation isdynamically analyzed to determine the second set of computing resourcesfor performing the simulation while achieving a target value for asimulation metric. The simulation metric is core hour cost, a memoryrequirement, simulation run time, efficiency of hardware configuration,or energy cost. Additionally, the target value for the simulation metricis an optimal value for the simulation metric.

Alternatively or additionally, each of the first and second sets ofcomputing resources includes at least one of a number of cores, anamount of memory, a number of virtual machines, or a hardwareconfiguration.

Alternatively or additionally, in some implementations, the methodoptionally includes transferring a state of the simulation from thefirst set of computing resources to the second set of computingresources. The state of the simulation includes at least one of meshinformation, constraint and loading conditions, derived quantities,factorized matrices, primary solution and secondary field variables,history variables, or stored results.

Alternatively or additionally, in some implementations, the at least oneattribute of the simulation is periodically analyzed to determine thesecond set of computing resources for performing the simulation.

Alternatively or additionally, the simulation is represented by a set ofequations. Optionally, the set of equations represents partialdifferential equations (PDEs).

Alternatively or additionally, in some implementations, the dynamicanalysis optionally includes comparing the at least one attribute of thesimulation to a threshold.

Alternatively or additionally, in some implementations, the first andsecond sets of computing resources are part of a computing cluster.

An example system for automated resource allocation during acomputational simulation is described herein. The system includes acomputing cluster, and a resource allocator operably coupled to thecomputing cluster. The resource allocator includes a processor and amemory operably coupled to the processor, where the memory hascomputer-executable instructions stored thereon. The resource allocatoris configured to analyze a set of simulation inputs to determine a firstset of computing resources in the computing cluster for performing asimulation. The first set of computing resources is configured to startthe simulation. Additionally, the resource allocator is configured todynamically analyze at least one attribute of the simulation todetermine a second set of computing resources in the computing clusterfor performing the simulation. The second set of computing resources isconfigured to perform the simulation. The second set of computingresources is different than the first set of computing resources.

Additionally, in some implementations, the step of dynamically analyzingthe at least one attribute of the simulation further determines that thesimulation requires more computing resources than included in the firstset of computing resources.

Alternatively or additionally, the set of simulation inputs includes atleast one of a geometry representation, a material property, a boundarycondition, a loading condition, a mesh parameter, a solver option, asimulation output request, or a time parameter.

Alternatively or additionally, the at least one attribute of thesimulation is a simulation requirement, a simulation performancecharacteristic, or compute capacity indicator. The compute capacityindicator includes at least one of a usage level of computing capacity,a memory bandwidth, a network bandwidth, or a network latency.

Optionally, in some implementations, respective simulation inputs foreach of a plurality of simulations are analyzed.

In some implementations, the step of performing the simulation with thesecond set of computing resources includes automatically restarting thesimulation with the second set of computing resources. Alternatively,the step of performing the simulation with the second set of computingresources includes automatically continuing the simulation with thesecond set of computing resources.

Alternatively or additionally, in some implementations, the resourceallocator is optionally configured to adaptively refine a mesh duringthe simulation. The adaptive refinement of the mesh includes changing amesh density and/or an order of mesh elements.

Alternatively or additionally, in some implementations, the set ofsimulation inputs is analyzed to determine the first set of computingresources for performing the simulation while achieving a target valuefor a simulation metric. Alternatively or additionally, in someimplementations, the at least one attribute of the simulation isdynamically analyzed to determine the second set of computing resourcesfor performing the simulation while achieving a target value for asimulation metric. The simulation metric is core hour cost, a memoryrequirement, simulation run time, efficiency of hardware configuration,or energy cost. Additionally, the target value for the simulation metricis an optimal value for the simulation metric.

Alternatively or additionally, each of the first and second sets ofcomputing resources includes at least one of a number of cores, anamount of memory, a number of virtual machines, or a hardwareconfiguration.

Alternatively or additionally, in some implementations, the resourceallocator is optionally configured to transfer a state of the simulationfrom the first set of computing resources to the second set of computingresources. The state of the simulation includes at least one of meshinformation, constraint and loading conditions, derived quantities,factorized matrices, primary solution and secondary field variables,history variables, or stored results.

Alternatively or additionally, in some implementations, the at least oneattribute of the simulation is periodically analyzed to determine thesecond set of computing resources for performing the simulation.

Alternatively or additionally, the simulation is represented by a set ofequations. Optionally, the set of equations represents partialdifferential equations (PDEs).

Alternatively or additionally, in some implementations, the dynamicanalysis optionally includes comparing the at least one attribute of thesimulation to a threshold.

Alternatively or additionally, in some implementations, the first andsecond sets of computing resources are part of a computing cluster.

It should be understood that the above-described subject matter may alsobe implemented as a computer-controlled apparatus, a computer process, acomputing system, or an article of manufacture, such as acomputer-readable storage medium.

Other systems, methods, features and/or advantages will be or may becomeapparent to one with skill in the art upon examination of the followingdrawings and detailed description. It is intended that all suchadditional systems, methods, features and/or advantages be includedwithin this description and be protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The components in the drawings are not necessarily to scale relative toeach other. Like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a block diagram of an example computing environment accordingto an implementation described herein.

FIG. 2 a flowchart illustrating example operations for automatedresource allocation for computational simulation according to animplementation described herein.

FIG. 3 is a diagram illustrating containerization according to animplementation described herein.

FIG. 4 is a flowchart illustrating example operations for dynamicanalysis of the simulation at each iterative time step according to animplementation described herein.

FIG. 5A illustrates an example simulation model where Regions 1, 2, and3 are meshed with a uniform structured grid. FIG. 5B illustrates anexample simulation model where Regions 1, 2, and 3 are meshed withstructured grid having different mesh densities. FIG. 5C is a diagramillustrating containerization for solving the simulation model of FIG.5B.

FIG. 6 is a block diagram of an example computing device.

DETAILED DESCRIPTION

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art. Methods and materials similar or equivalent to those describedherein can be used in the practice or testing of the present disclosure.As used in the specification, and in the appended claims, the singularforms “a,” “an,” “the” include plural referents unless the contextclearly dictates otherwise. The term “comprising” and variations thereofas used herein is used synonymously with the term “including” andvariations thereof and are open, non-limiting terms. The terms“optional” or “optionally” used herein mean that the subsequentlydescribed feature, event or circumstance may or may not occur, and thatthe description includes instances where said feature, event orcircumstance occurs and instances where it does not. Ranges may beexpressed herein as from “about” one particular value, and/or to “about”another particular value. When such a range is expressed, an aspectincludes from the one particular value and/or to the other particularvalue. Similarly, when values are expressed as approximations, by use ofthe antecedent “about,” it will be understood that the particular valueforms another aspect. It will be further understood that the endpointsof each of the ranges are significant both in relation to the otherendpoint, and independently of the other endpoint.

Described herein are systems and methods for automated resourceallocation during a computational simulation (also referred to herein as“numerical simulation” or “simulation”). As described herein, thesystems and methods improve the robustness and efficiency of simulationwhen using parallel computing resources to calculate a solution for avirtual model of a physical object or phenomenon. Using conventionaltechniques, it is difficult to determine a priori the required set ofcomputing resources for a simulation, and particularly an optimal and/orminimal set of computing resources. In other words, a priori knowledgeof the simulation alone may be insufficient to accurately determine therequired computing resources for the simulation. Once the simulation isstarted, additional information, which is unknown at the start, iscollected during the simulation. For example, using conventionaltechniques, a user may estimate that “X” gigabytes (GB) of memory arerequired for a simulation. The simulation is started with “X” GB ofmemory available, but due to unknown or unknowable factors at starttime, the simulation will actually require more than “X” GB of memory toreach completion. This this will cause the simulation to fail before itis finished. Alternatively, the simulation may actually require lessthan “X” GB of memory, which needlessly ties up computing resources.Conventional techniques do not automatically detect and respond to suchsimulation states.

The systems and methods described herein address the problems above, forexample by automating resource allocation. For example, the systems andmethods described herein improve robustness by avoiding simulationfailure due to inadequate resource allocation. By performing a dynamicanalysis while the simulation is running, the computing resourcedetermination is updated using a posteriori knowledge of the simulation.As a result, the systems and method described herein are capable ofpreventing simulation failure before it occurs (i.e., the systems andmethods described herein are proactive, not simply reactive to adetected failure). The systems and methods described herein also improveefficiency by correcting over-allocation of computing resources. Thesystems and methods described herein also account for changes to therequired resources during the simulation. These capabilities representan improvement over manually determining the resource requirements,reallocating resources and restarting a simulation.

Simulation methods include, but are not limited to, FEM and FDM. Forexample, the concept of finite element analysis (FEA) is generallywell-understood in the art and involves the discretization of a virtualmodel into nodes, each node containing spatial information as well asconnection to the surrounding nodes through differential equations(e.g., partial differential equations (PDEs)) that represent the physicsbeing calculated for that node. These nodes, and the differentialequations describing them, form a matrix that is representative of thevirtual model, and the matrix is transmitted in whole or in part to aprocessing unit or group of processing units for calculation of asolution at a given time or frequency (or time range or set offrequencies).

Optionally, in an elastic cloud computing system (e.g., the computingenvironment shown in FIG. 1 ), the optimal amount of computationalresources (e.g. number of computational cores, amount of memory, type ofhardware, etc.) can be dynamically determined and chosen to optimallysolve either the single simulation or several separate simulations. In acloud computing environment, the optimal amount of computing resourcesto assign to a single simulation or a set of simulations can be chosento optimize based on different criteria, e.g.:

minimize core hour cost;

minimize total simulation time;

maximize packing efficiency for a given hardware configuration; and/or

minimize energy used.

As described below, dynamically changing the resources used for asimulation in a cloud computing environment may include increasing orreducing the resources (cores, RAM, etc.) allocated to a singlecontainer or starting a new container of different size and mapping thesimulation state from the original container into the new container,where the simulation is either continued or restarted with the newcontainer.

Referring now to FIG. 1 , an example computing environment is shown. Themethods for dynamic resource allocation for computational simulationdescribed herein can be performed using the computing environment shownin FIG. 1 . The environment includes a simulation device 110, a resourceallocator 120, an originating device 140, and an observer 150. It shouldbe understood that the environment shown in FIG. 1 is provided only asan example. This disclosure contemplates that the methods for dynamicresource allocation for computational simulation described herein may beperformed using a computing environment having more or less componentsand/or with components arranged differently than shown in FIG. 1 . Itshould be understood that the logical operations described herein can beperformed by one or more of the devices shown in FIG. 1 , which isprovided only as an example computing environment.

The simulation device 110, the resource allocator 120, the originatingdevice 140, and the observer 150 are operably coupled to one or morenetworks 130. This disclosure contemplates that the networks 130 are anysuitable communication network. The networks 130 can be similar to eachother in one or more respects. Alternatively or additionally, thenetworks 130 can be different from each other in one or more respects.The networks 130 can include a local area network (LAN), a wirelesslocal area network (WLAN), a wide area network (WAN), a metropolitanarea network (MAN), a virtual private network (VPN), etc., includingportions or combinations of any of the above networks. Additionally,each of the simulation device 110, the resource allocator 120, theoriginating device 140, and the observer 150 are coupled to the one ormore networks 130 through one or more communication links. Thisdisclosure contemplates the communication links are any suitablecommunication link. For example, a communication link may be implementedby any medium that facilitates data exchange including, but not limitedto, wired, wireless and optical links. Example communication linksinclude, but are not limited to, a LAN, a WAN, a MAN, Ethernet, theInternet, or any other wired or wireless link such as WiFi, WiMax, 3G,4G, or 5G.

The simulation device 110 can be a computing cluster, for example, madeup of a plurality of nodes 115 (e.g., the nodes 115A, 115B, and 115C).As used herein, a computing cluster is a plurality of inter-connectedcomputing resources that are accessible over a network and haveresources (e.g., computing power, data storage, etc.) greater than thosefound in a typical personal computer. In some implementations, thecomputing cluster is a cloud-based computing cluster. Cloud-basedcomputing is an on-demand computing environment where tasks areperformed by remote resources (e.g., processing units, storage,databases, software, etc.) that are linked to a user (e.g., theoriginating device 140) through a communication network (e.g., theInternet) or other data transmission medium. Cloud-based computing iswell known in the art and is therefore not described in further detailherein. In other implementations, the computing cluster is a localcomputing cluster (e.g., computing assets linked via a LAN), whereresources are linked to a user (e.g., the originating device 140)through a communication network (e.g., the LAN) or other datatransmission medium. Each node 115 can be made up of one or morecomputing devices such as the computing device 600 shown in FIG. 6 . Itshould be understood that the number of nodes 115 (i.e., three) in FIG.1 is provided for illustrative purposes only. There is no limit to thenumber of nodes 115 that can be supported by the simulation device 110.The simulation device 110 can be configured to perform the computationalsimulation (e.g., FEM, FDM, or other computational simulationtechnique). Example systems and methods for running a simulation using acloud-based computing cluster are described in U.S. patent applicationSer. No. 16/856,222, filed Apr. 23, 2020 by OnScale, Inc., and titled“SYSTEMS AND METHODS FOR RUNNING A SIMULATION.”

The resource allocator 120 can be a computing device such as thecomputing device 600 shown in FIG. 6 . The resource allocator 120 can beconfigured to execute an application 122. The application 122 mayinclude instructions for executing one or more of the operations forautomated resource allocation for computational simulation as describedwith regard to FIG. 2 . For example, the resource allocator 120 can beconfigured to receive and/or access information associated with thesimulation(s) (e.g., information including, but not limited to, thesimulation inputs, simulation attributes, and/or compute capacityindicators described herein), analyze such information associated withthe simulation(s), and/or allocate computing resources based on suchanalysis. Such information associated with the simulation(s) can bereceived from a monitoring device or process. Optionally, the resourceallocator 120 can be configured to monitor information associated withthe simulation(s) (e.g., information including, but not limited to, thesimulation inputs, simulation attributes, and/or compute capacityindicators described herein). The resource allocator 120 can communicatewith the networks 130 through a network interface 124. Optionally, thenetwork interface 124 may encrypt data prior to transmitting such datavia the networks 130. This disclosure contemplates that any type ofencryption may be used.

The originating device 140 can be a computing device such as thecomputing device 600 shown in FIG. 6 . The originating device 140 may bea computing device associated with a user such as a personal computer,desktop computer, laptop, tablet, etc. The originating device 140 can beconfigured to execute an application 142. The application 142 may be anengineering application (e.g., CAD application) or any other type ofapplication that incorporates modelling, modelling data, simulations,and/or simulation data. The originating device 140 can request that acomputational simulation be performed by the simulation device 110. Theoriginating device 140 can communicate with the networks 130 through anetwork interface 144. Optionally, the network interface 144 may encryptdata prior to transmitting such data via the networks 130. Thisdisclosure contemplates that any type of encryption may be used.

The observer 150 can be a computing device such as the computing device600 shown in FIG. 6 . The observer 150 can be configured to execute anapplication 152. The application 152 may include instructions forexecuting one or more of the operations for automated resourceallocation for computational simulation as described with regard to FIG.2 . For example, the observer 150 can be configured to execute a processmonitoring application. In other words, the observer 150 can beconfigured to monitor information associated with the simulation(s)(e.g., information including, but not limited to, the simulationattributes and/or compute capacity indicators described herein). Theobserver 150 can communicate with the networks 130 through a networkinterface 154. Optionally, the network interface 154 may encrypt dataprior to transmitting such data via the networks 130. This includes, butis not limited to, transmitting monitored information to the resourceallocator 120, which can be configured to analyze the monitoredinformation. This disclosure contemplates that any type of encryptionmay be used.

Referring now to FIG. 2 , a flowchart illustrating example operationsfor automated resource allocation for computational simulation is shown.The example operations shown in FIG. 2 can be performed in the computingenvironment shown in FIG. 1 . For example, in some implementations, theexample operations can be performed by the resource allocator 120 and/orthe simulation device 110 shown in FIG. 1 . Optionally, in someimplementations, the example operations can be performed by the resourceallocator 120, the observer 130, and/or the simulation device 110 shownin FIG. 1 . As described below, one or more of the operations shown inFIG. 2 can be performed automatically, e.g., without user input and/orintervention. For example, once the simulation begins (e.g., step 204 ofFIG. 2 ), a dynamic analysis is performed (e.g., step 206 of FIG. 2 )and the computing resources are adjusted (e.g., step 208 of FIG. 2 ) independence on the results of the dynamic analysis. In other words, nouser input or intervention is required to adjust the computingresources. Optionally, in some implementations, all of the operationsshown in FIG. 2 can be performed automatically, e.g., without user inputand/or intervention.

At step 202, a set of simulation inputs is analyzed to determine a firstset of computing resources for performing a simulation. The analysis ofstep 202 is based on a priori knowledge of the simulation. As describedherein, the simulation provides a numerical solution for a simulationmodel, which is a representation of a physical object. The simulationmodel is a two-dimensional (2D) model or a three-dimensional (3D) model.For example, the simulation model may be used to simulate variousmechanical, thermal, thermomechanical, electromechanical, fluid flowdynamics, and/or magnetomechanical aspects of the physical object. Asdescribed herein, the simulation may be performed using the simulationdevice 110 shown in FIG. 1 , which is a computing cluster. Additionally,the simulation is represented by a set of element equations. The elementequations may be differential equations such as PDEs. Computationsimulation techniques such as FEM and FDM may be used to obtain anumerical solution for a set of differential equations. As used herein,a set of simulation inputs includes one or more simulation inputs.Simulation inputs can include, but are not limited to, geometryrepresentations (e.g., CAD files, image files), material properties(e.g., density, heat capacity, Young's modulus), boundary conditions(e.g., fluid velocity, solid wall of fluid channel, pressure,displacement), loading conditions (e.g., force, pressure, heat flux,temperature), mesh parameters (e.g., mesh cell size, mesh cell elementtype), solver options (e.g., steady state, transient), output requestsand/or time parameters. It should be understood that the simulationinputs (and examples thereof) provided above are only examples. Thisdisclosure contemplates that the simulation inputs analyzed at step 202may include any information, data, etc. needed and/or desired to run asimulation.

Optionally, in some implementations, respective simulation inputs foreach of a plurality of simulations are analyzed at step 202. In theseimplementations, each of the simulations provides a numerical solutionfor a respective simulation model, which is represented by a respectiveset of element equations. For example, the simulation model mayoptionally be partitioned into multiple windows (e.g., by physics, solvemethod, and/or time step size), each window being represented by adifferent set of element equations. In these implementations, theanalysis at step 202 can be used to determine a respective set ofcomputing resources for solving a respective simulation to arrive at itsnumerical solution.

As described above, step 202, which can be performed by the resourceallocator 120 shown in FIG. 1 , analyzes the simulation inputs todetermine a set of computing resources (e.g., a number of cores, amountof RAM, etc.) needed to perform the simulation. It should be understoodthat the resource allocator 120 shown in FIG. 1 can be configured toreceive and/or access the simulation inputs. Optionally, the set ofsimulation inputs is analyzed to determine the set of computingresources for performing the simulation while achieving a target valuefor a simulation metric. Optionally, the target value is an optimalvalue for the simulation metric. In other words, the resource allocator120 can, in some implementations, determine the set of computingresources needed to optimize the simulation, for example, to minimizeone or more of the cost, time, and/or resources used for the simulation.In other implementations, the target value is a desired value for thesimulation metric (i.e., not optimal but desired). For example, the usermay provide a desired cost limitation and/or desired run timerequirement. This disclosure contemplates that a simulation metric caninclude, but is not limited to, core hour cost, simulation run time,efficiency of hardware configuration, or energy cost. It should beunderstood that these are only example simulation metrics. Thisdisclosure contemplates determining a set of computing resources neededto achieve a target value for other simulation metrics. Optionally, insome implementations, the objective is to solve multiple sets of elementequations in about the same amount of time (e.g., achieve the same orsimilar simulation run time for multiple simulations). As describedherein, the simulation is performed by a computing cluster, and thecomputing resources for performing the simulation can be assigned and/oradjusted to achieve the simulation metric. This adjustment can occurdynamically, e.g., during the simulation as described below. In otherwords, the number of processing units and/or memory assigned from thecomputing cluster can be increased or decreased to achieve thesimulation metric.

This disclosure contemplates that the analysis of step 202 of FIG. 2 canbe performed using a model for estimating the required computingresources based on one or more known simulation inputs. Such modelsinclude, but are not limited to, machine learning models, empiricalmodels, and analytical models. An example method for analyzing asimulation to estimate the computational cost of simulation usingmachine learning is described in in U.S. Provisional Patent App. No.62/931,299, filed Nov. 6, 2019 by OnScale, Inc., and titled “METHODS FORTHE ESTIMATION OF THE COMPUTATIONAL COST OF SIMULATION.” It should beunderstood that the machine learning-based method described in U.S.Provisional Patent App. No. 62/931,299 is provided only as an exampletechnique for performing the analysis of step 202 of FIG. 2 . Thisdisclosure contemplates using other techniques for analyzing the set ofsimulation inputs to determine a set of computing resources needed toperform the simulation. For example, empirical, semi-empirical oranalytical models can be used to estimate the resources (e.g., cores,memory, time, etc.) needed by an algorithm to solve a givencomputational problem. This disclosure contemplates using empirical,semi-empirical or analytical models known in the art to estimate theresources. As a non-limiting example, the model may be a best fitregression model. A regression model may be linear or non-linear. Anexample regression model can estimate computational cost based on thesimulation inputs, e.g., the mesh size (e.g., number of cells and/orvertices) and the geometric parameters (e.g., surface-to-volume ratio).It should be understood that the simulation inputs upon which theexample regression model is based are provided only as an example.

A set of computing resources can include, but is not limited to, anumber of cores, an amount of memory (e.g., RAM), a number of virtualmachines, and/or a hardware configuration. For example, the first set ofcomputing resources may be the computing resources of Container A 302shown in FIG. 3 . Container A 302 includes a given number of cores andamount of memory needed to solve a simulation model. This disclosurecontemplates that computing resources from the computing cluster shownin FIG. 1 can be used to create Container A 302. Optionally, the firstset of computing resources is an optimal set of computing resources forsolving the set of element equations to arrive at the numerical solutionfor the simulation while achieving a target value for a simulationmetric (e.g., cost, run time, energy, etc.).

Referring again to FIG. 2 , at step 204, the simulation model is startedwith the first set of computing resources. For example, the first set ofcomputing resources may be the computing resources of Container A 302shown in FIG. 3 . As described herein, the simulation is performed by acomputing cluster such as the simulation device 110 shown in FIG. 1 . Insome implementations, performance of the simulation at step 204 isstarted automatically, e.g., without user input and/or intervention andin response to completion of step 202. Alternatively, in otherimplementations, performance of the simulation at step 204 is startedmanually, e.g., with user input and/or intervention following completionof step 202.

Referring again to FIG. 2 , at step 206, at least one attribute of thesimulation is dynamically analyzed to determine a second set ofcomputing resources for performing the simulation. The analysis of step206 can use a posteriori knowledge of the simulation. Additionally, asdescribed herein, the dynamic analysis of step 206 makes the automatedprocess proactive, not reactive. In other words, the objective of step206 is to dynamically analyze the simulation attribute(s) while thesimulation is running and proactively determine a set of computingresources for performing the simulation. This set of computing resourcesmay be more or less than those currently running the simulation. Thedynamic analysis of step 206 can therefore be used to make adjustments.It should be understood that the simulation may end up requiring more orless computing resources than determined at step 202. For example, thesimulation may be more or less computationally intense than expected.This may not be determined until the simulation is already running. Forexample, the dynamic analysis at step 206 considers attributes of thesimulation, which is running, while the analysis at step 202 considerssimulation inputs. In some implementations, a single attribute of thesimulation is analyzed at step 206. Alternatively, in otherimplementations, multiple attributes of the simulation are analyzed atstep 206. As used herein, a dynamic analysis is performed duringperformance of the simulation. For example, the dynamic analysis of step206 can be performed during performance of the simulation with the firstset of computing resources, i.e., while the simulation is running.Dynamic analysis of the attribute(s) of the simulation at step 206 canoccur automatically, e.g., without user input and/or intervention andwhile the simulation is running.

As described above, the dynamic analysis of step 206 can be performed bythe resource allocator 120 shown in FIG. 1 . It should be understoodthat the resource allocator 120 shown in FIG. 1 can also be configuredto receive, access, and/or monitor the at least one attribute of thesimulation. Additionally, as used herein, an attribute of the simulationcan include, but is not limited to, simulation requirements (e.g.,amount of memory), simulation performance characteristics (e.g., memoryor processor usage), and compute capacity indicators. Compute capacityindicators can include, but are not limited to, usage levels ofprocessor capacity, memory bandwidth, network bandwidth, and/or level ofnetwork latency and may optionally be related to an expected quality ofservice. This disclosure contemplates monitoring one or more attributesof the simulation using the computing environment shown in FIG. 1 . Forexample, this disclosure contemplates that the resource allocator 120and/or the observer 150 shown in FIG. 1 can be configured to monitorattributes of the simulation such as compute capacity indicators, forexample, by running a process monitoring application. Process monitoringapplications are known in the art and are therefore not described infurther detail herein. Alternatively, compute capacity indicators suchas usage levels can be monitored by measurements within the simulationprogram such as through operating system function calls.

Additionally, in some implementations, the dynamic analysis of step 206includes determining a difference between a required computing resourceand an available computing resource. This can be accomplished, forexample, by determining a difference between an attribute of thesimulation (e.g., a monitored simulation requirement, simulationperformance characteristic, or compute capacity indicator), which mayrepresents the required computing resource, and the first set ofcomputing resources, which may represent the available computingresources. If the required computing resources exceed or are less thanthe available computing resources, then the computing resources (e.g.,the first set of computing resources) can be modified accordingly. Forexample, a number of cores, an amount of memory (e.g., RAM), a number ofvirtual machines, and/or a hardware configuration can be determined asthe second set of computing resources for performing the simulation.Optionally, a number of cores, an amount of memory (e.g., RAM), a numberof virtual machines, and/or a hardware configuration can be assigned orremoved from the first set of computing resources. In other words, thechange (e.g., increase, decrease) in computing resources may betriggered in response to dynamic analysis of the at least one simulationattribute, for example, in order to meet demand and/or respond toexisting conditions. Alternatively or additionally, the dynamic analysisof step 206 optionally includes comparing an attribute of the simulationto a threshold. It should be understood that this may not involvedetermining a difference between required and available computingresources. If the attribute of the simulation exceeds or is less thanthe threshold, then the computing resources (e.g., the first set ofcomputing resources) can be modified accordingly. Resource modificationcan occur automatically, e.g., without user input and/or intervention.It should be understood that the attributes of the simulation (andexamples thereof) provided above are only examples. This disclosurecontemplates that the attributes of the simulation analyzed at step 206may include any information, data, etc. associated with the runningsimulation.

The second set of computing resources is different than the first set ofcomputing resources. The second set of computing resources may contain adifferent number of cores, amount of memory (e.g., RAM), number ofvirtual machines, and/or a hardware configuration than the first set ofcomputing resources. It should be understood that the first and secondset of computing resources may have specific cores, memory, virtualmachines, etc. in common. In some implementations, the second set ofcomputing resources is greater than (e.g., more computing power and/ormore memory) the first set of computing resources. For example, in someimplementations, the dynamic analysis further determines that thesimulation requires more computing resources than included in the set ofcomputing resources currently performing the simulation (e.g., the firstset of computing resources determined at step 202). In this scenario,the current set of computing resources are insufficient, i.e., thecurrent set of computing resources cannot complete the simulation.Without intervention, the simulation will fail. To avoid this outcomebefore it occurs, additional computing resources (e.g., the second setof computing resources determined at step 206) can therefore be used toperform the simulation. In other implementations, the second set ofcomputing resources is less than (e.g., less computing power and/or lessmemory) the first set of computing resources. For example, in someimplementations, the dynamic analysis further determines that thesimulation requires less computing resources than included in the set ofcomputing resources currently performing the simulation (e.g., the firstset of computing resources determined at step 202). In this scenario,the current set of computing resources are sufficient, i.e., the currentset of computing resources can complete the simulation, but the currentset of resources may be more expensive (e.g., too many, too muchcomputing power and/or memory, too fast, etc.) than desired. Fewercomputing resources (e.g., the second set of computing resourcesdetermined at step 206) can therefore be used to perform the simulation.

Optionally, the dynamic analysis of the attribute(s) of the simulationdetermines the set of computing resources for performing the simulationwhile achieving a target value for a simulation metric. As describedabove, the target value is optionally an optimal value for thesimulation metric. Alternatively, the target value is optionally adesired value for the simulation metric. This disclosure contemplatesthat a simulation metric can include, but is not limited to, core hourcost, simulation run time, efficiency of hardware configuration, orenergy cost. It should be understood that these are only examplesimulation metrics.

Example analysis methods are described above with regard to step 202.Analysis method include, but are not limited to, machine learningmodels, empirical models, and analytical models. This disclosurecontemplates that the same and/or different analysis methods can be usedat step 206. Optionally, in step 206, the analysis method can includethe current and historical attributes of the simulation (e.g., aposteriori knowledge of the simulation), which may be in addition to thesimulation inputs analyzed at step 202 (e.g., a priori knowledge of thesimulation). In other words, the analysis of step 206 can optionallyaccount for data obtained from running the simulation. As describedabove, the current and historical attributes of the simulation, whichare obtained by running the simulation, can provide additional data thatmay be useful in determining the set of computing resources. Suchadditional information is unknown before beginning of the simulation.Optionally, the attribute(s) of the simulation are periodically analyzedto determine the second set of computing resources. For example, thedynamic analysis of the attribute(s) of the simulation can be performedbetween time iterations. Such a process is shown, for example, in theflowchart of FIG. 4 . Alternatively, the dynamic analysis of theattribute(s) of the simulation can be performed in the frequency domainor on a quasi-static process.

The second set of computing resources may be the computing resources ofContainer B 304 shown in FIG. 3 . Container B 304 includes a givennumber of cores and amount of memory needed to solve the simulationmodel. This disclosure contemplates that computing resources from thecomputing cluster shown in FIG. 1 can be used to create Container B 304.Optionally, the second set of computing resources is an optimal set ofcomputing resources for solving the set of element equations to arriveat the numerical solution for the simulation while achieving a targetvalue for a simulation metric (e.g., cost, run time, energy, etc.).

Referring again to FIG. 3 , two different containers—Container A 302 andContainer B 304—are shown. Container A 302 may be the first set ofcomputing resources described herein, e.g., the set of computingresources performing the simulation at step 204. This represents thecurrent state of the simulation. Container B 304 may be the second setof computing resources described herein, e.g., the set of computingresources performing the simulation at step 208. This represents thefuture state of the simulation. As shown by reference number 306 in FIG.3 , a new container (e.g., Container B 304) can be created. Thiscontainer can include the second set of computing resources describedherein, which is different than the first set of computing resources.The simulation state can be transferred from the first set of computingresources (e.g., Container A 302) to the second set of computingresources (e.g., Container B 304) by moving or copying the simulationdata from program memory to persistent memory in Container A 302. Thepersistent memory representation of the simulation data can be connectedto or parsed by Container B 304. For example, as shown in FIG. 3 ,Container A 302 and Container B 304 have access to a file system. Thefile system is used to temporarily store the contents of Container A 302until such contents can be transferred to Container B 304. It should beunderstood that a file system is provided only as an example means formoving or transferring simulation data from Container A 302 to ContainerB 304. The simulation state can include, but is not limited to, meshinformation, constraint and loading conditions, derived quantities,factorized matrices, primary solution and secondary field variables,history variables and stored results.

Referring again to FIG. 2 , at step 208, the simulation is performedwith the second set of computing resources. For example, the second setof computing resources may be the computing resources of Container B 304shown in FIG. 3 . As described herein, the simulation is performed by acomputing cluster such as the simulation device 110 shown in FIG. 1 . Insome implementations, the simulation is restarted using the second setof computing resources. In other words, the simulation is restarted fromthe beginning using the second set of computing resources.Alternatively, in other implementations, performance of the simulationis continued using the second set of computing resources. In otherwords, the simulation is continued beginning at the point where thefirst set of computing resources stopped the simulation, e.g., the nexttime iteration or frequency. In either case, the simulation with thefirst set of computing resources can be terminated in favor of thesimulation with the second set of computing resources. Performance ofthe simulation at step 208 occurs automatically, e.g., without userinput and/or intervention and in response to completion of step 206.

Optionally, in some implementations, the mesh is adaptively refinedduring performance of the simulation. As described herein, the domain ofthe simulation model is discretized into a finite number of elements (orpoints, cells) called a mesh. Adaptive refinement of the mesh includeschanging a mesh density or an order of mesh elements. Alternatively oradditionally, adaptive refinement of the mesh includes changing both themesh density and the order of mesh elements. Adaptive mesh refinementtechniques are known in the art and include, but are not limited to,h-adaptivity, p-adaptivity, and hp-adaptivity. It should be understoodthat at least one of a domain size, a number of degrees of freedom(DoF), or a constraint condition is changed as a result of the adaptiverefinement of the mesh. And as a result, dynamic resource allocation forcomputational simulation described with regard to FIG. 2 may beadvantageous.

FIG. 5A illustrates an example where Regions 1, 2, and 3 of a simulationmodel are meshed with a uniform structured grid. This disclosurecontemplates that the simulation model of FIG. 5A can be performed usingone or more simulation devices such as simulation device 110 shown inFIG. 1 . For example, a uniform structured grid uses a standard cellsize and shape (known as a voxel) to allow for efficient indexing ofelements in order to reduce the required memory and compute time. Thisapproach is limited, however, in that it complicates spatial refinementof the mesh to improve accuracy and/or necessitates numerical techniquesthat may themselves be computationally expensive. Accordingly, it may bedesirable in some implementations to use different mesh densities forRegions 1, 2, and 3. This is shown, for example, in FIG. 5B, where thesimulation model is decomposed into constituent parts and a structuredgrid mesh with different mesh refinements for each of Regions 1, 2, and3 is applied. It should be understood that information on the regionboundaries can be coupled for the purposes of the simulation. Using thetechniques described herein, different containers can be created toperform simulations for Regions 1, 2, and 3 shown in FIG. 5B. This isshown in FIG. 5C, where simulations for Regions 1, 2, and 3 are assignedto Computers 1, 2, and 3, respectively, each of which is made up ofdifferent computing resources. The respective containers can be createdand assigned based on the analysis to determine a set of computingresources for solving each respective simulation model to arrive at thenumerical solution while achieving a simulation metric (e.g., core hourcost, simulation run time, efficiency of hardware configuration, orenergy cost). It should be understood that discretizing the simulationmodel domain spatially as shown in FIGS. 6A-6C is provided only as anexample. This disclosure contemplates discretizing the simulation modeldomain by physics, solve type, time step, etc.

It should be appreciated that the logical operations described hereinwith respect to the various figures may be implemented (1) as a sequenceof computer implemented acts or program modules (i.e., software) runningon a computing device (e.g., the computing device described in FIG. 6 ),(2) as interconnected machine logic circuits or circuit modules (i.e.,hardware) within the computing device and/or (3) a combination ofsoftware and hardware of the computing device. Thus, the logicaloperations discussed herein are not limited to any specific combinationof hardware and software. The implementation is a matter of choicedependent on the performance and other requirements of the computingdevice. Accordingly, the logical operations described herein arereferred to variously as operations, structural devices, acts, ormodules. These operations, structural devices, acts and modules may beimplemented in software, in firmware, in special purpose digital logic,and any combination thereof. It should also be appreciated that more orfewer operations may be performed than shown in the figures anddescribed herein. These operations may also be performed in a differentorder than those described herein.

Referring to FIG. 6 , an example computing device 600 upon which themethods described herein may be implemented is illustrated. It should beunderstood that the example computing device 600 is only one example ofa suitable computing environment upon which the methods described hereinmay be implemented. Optionally, the computing device 600 can be awell-known computing system including, but not limited to, personalcomputers, servers, handheld or laptop devices, multiprocessor systems,microprocessor-based systems, network personal computers (PCs),minicomputers, mainframe computers, embedded systems, and/or distributedcomputing environments including a plurality of any of the above systemsor devices. Distributed computing environments enable remote computingdevices, which are connected to a communication network or other datatransmission medium, to perform various tasks. In the distributedcomputing environment, the program modules, applications, and other datamay be stored on local and/or remote computer storage media.

In its most basic configuration, computing device 600 typically includesat least one processing unit 606 and system memory 604. Depending on theexact configuration and type of computing device, system memory 604 maybe volatile (such as random access memory (RAM)), non-volatile (such asread-only memory (ROM), flash memory, etc.), or some combination of thetwo. This most basic configuration is illustrated in FIG. 6 by dashedline 602. The processing unit 606 may be a standard programmableprocessor that performs arithmetic and logic operations necessary foroperation of the computing device 600. The computing device 600 may alsoinclude a bus or other communication mechanism for communicatinginformation among various components of the computing device 600.

Computing device 600 may have additional features/functionality. Forexample, computing device 600 may include additional storage such asremovable storage 608 and non-removable storage 610 including, but notlimited to, magnetic or optical disks or tapes. Computing device 600 mayalso contain network connection(s) 616 that allow the device tocommunicate with other devices. Computing device 600 may also have inputdevice(s) 614 such as a keyboard, mouse, touch screen, etc. Outputdevice(s) 612 such as a display, speakers, printer, etc. may also beincluded. The additional devices may be connected to the bus in order tofacilitate communication of data among the components of the computingdevice 600. All these devices are well known in the art and need not bediscussed at length here.

The processing unit 606 may be configured to execute program codeencoded in tangible, computer-readable media. Tangible,computer-readable media refers to any media that is capable of providingdata that causes the computing device 600 (i.e., a machine) to operatein a particular fashion. Various computer-readable media may be utilizedto provide instructions to the processing unit 606 for execution.Example tangible, computer-readable media may include, but is notlimited to, volatile media, non-volatile media, removable media andnon-removable media implemented in any method or technology for storageof information such as computer readable instructions, data structures,program modules or other data. System memory 604, removable storage 608,and non-removable storage 610 are all examples of tangible, computerstorage media. Example tangible, computer-readable recording mediainclude, but are not limited to, an integrated circuit (e.g.,field-programmable gate array or application-specific IC), a hard disk,an optical disk, a magneto-optical disk, a floppy disk, a magnetic tape,a holographic storage medium, a solid-state device, RAM, ROM,electrically erasable program read-only memory (EEPROM), flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices.

In an example implementation, the processing unit 606 may executeprogram code stored in the system memory 604. For example, the bus maycarry data to the system memory 604, from which the processing unit 606receives and executes instructions. The data received by the systemmemory 604 may optionally be stored on the removable storage 608 or thenon-removable storage 610 before or after execution by the processingunit 606.

It should be understood that the various techniques described herein maybe implemented in connection with hardware or software or, whereappropriate, with a combination thereof. Thus, the methods andapparatuses of the presently disclosed subject matter, or certainaspects or portions thereof, may take the form of program code (i.e.,instructions) embodied in tangible media, such as floppy diskettes,CD-ROMs, hard drives, or any other machine-readable storage mediumwherein, when the program code is loaded into and executed by a machine,such as a computing device, the machine becomes an apparatus forpracticing the presently disclosed subject matter. In the case ofprogram code execution on programmable computers, the computing devicegenerally includes a processor, a storage medium readable by theprocessor (including volatile and non-volatile memory and/or storageelements), at least one input device, and at least one output device.One or more programs may implement or utilize the processes described inconnection with the presently disclosed subject matter, e.g., throughthe use of an application programming interface (API), reusablecontrols, or the like. Such programs may be implemented in a high levelprocedural or object-oriented programming language to communicate with acomputer system. However, the program(s) can be implemented in assemblyor machine language, if desired. In any case, the language may be acompiled or interpreted language and it may be combined with hardwareimplementations.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed:
 1. A computer-implemented method for automated resourceallocation during a computational simulation, comprising: analyzing aset of simulation inputs to determine a first set of computing resourcesin a computing cluster for performing a simulation, wherein the firstset of computing resources is configured to start the simulation;starting the simulation with the first set of computing resources;dynamically analyzing at least one attribute of the simulation todetermine a second set of computing resources in the computing clusterfor performing the simulation, wherein the second set of computingresources includes a different number, amount, or type of computingprocessing units or memory than the first set of computing resources;and performing the simulation with the second set of computingresources.
 2. The computer-implemented method of claim 1, whereindynamically analyzing the at least one attribute of the simulationfurther determines that the simulation requires more computingprocessing units or memory than included in the first set of computingresources.
 3. The computer-implemented method of claim 1, wherein theset of simulation inputs comprises at least one of a geometryrepresentation, a material property, a boundary condition, a loadingcondition, a mesh parameter, a solver option, a simulation outputrequest, or a time parameter.
 4. The computer-implemented method ofclaim 1, wherein the at least one attribute of the simulation comprisesa simulation requirement, a simulation performance characteristic, or acompute capacity indicator.
 5. The computer-implemented method of claim1, wherein respective simulation inputs for each of a plurality ofsimulations are analyzed.
 6. The computer-implemented method of claim 1,wherein performing the simulation with the second set of computingresources comprises automatically restarting the simulation with thesecond set of computing resources.
 7. The computer-implemented method ofclaim 1, wherein performing the simulation with the second set ofcomputing resources comprises automatically continuing the simulationwith the second set of computing resources.
 8. The computer-implementedmethod claim 1, further comprising adaptively refining a mesh during thesimulation, wherein the adaptive refinement of the mesh compriseschanging a mesh density and/or an order of mesh elements.
 9. Thecomputer-implemented method of claim 1, wherein the set of simulationinputs is analyzed to determine the first set of computing resources forperforming the simulation while achieving a target value for asimulation metric.
 10. The computer-implemented method of claim 1,wherein the at least one attribute of the simulation is dynamicallyanalyzed to determine the second set of computing resources forperforming the simulation while achieving a target value for asimulation metric.
 11. The computer-implemented method of 9, wherein thesimulation metric is core hour cost, a memory requirement, simulationrun time, efficiency of hardware configuration, or energy cost.
 12. Thecomputer-implemented method of claim 11, wherein the target value forthe simulation metric is an optimal value for the simulation metric. 13.The computer-implemented method of claim 1, further comprisingtransferring a state of the simulation from the first set of computingresources to the second set of computing resources.
 14. Thecomputer-implemented method of claim 13, wherein the state of thesimulation comprises at least one of mesh information, constraint andloading conditions, derived quantities, factorized matrices, primarysolution and secondary field variables, history variables, or storedresults.
 15. The computer-implemented method of claim 1, wherein the atleast one attribute of the simulation is periodically analyzed todetermine the second set of computing resources for performing thesimulation.
 16. The computer-implemented method of claim 1, wherein thesimulation is represented by a set of equations.
 17. Thecomputer-implemented method of claim 16, wherein the set of equationsrepresents partial differential equations (PDEs).
 18. Thecomputer-implemented method of claim 1, wherein dynamically analyzing atleast one attribute of the simulation to determine a second set ofcomputing resources for performing the simulation comprises comparingthe at least one attribute of the simulation to a threshold.
 19. Asystem for automated resource allocation during a computationalsimulation, comprising: a computing cluster; and a resource allocatoroperably coupled to the computing cluster, the resource allocatorcomprising a processor and a memory operably coupled to the processor,wherein the memory has computer-executable instructions stored thereonthat, when executed by the processor, cause the processor to: analyze aset of simulation inputs to determine a first set of computing resourcesin the computing cluster for performing a simulation, wherein the firstset of computing resources is configured to start the simulation; anddynamically analyze at least one attribute of the simulation todetermine a second set of computing resources in the computing clusterfor performing the simulation, wherein the second set of computingresources including a different number, amount, or type of computingprocessing units or memory than the first set of computing resources,and wherein the second set of computing resources is configured toperform the simulation.